This model is a fine-tuned version of Qwen/Qwen2.5-7B on the assimilation_strict_json_v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.0000

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 2
seed: 13
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0000	0.6711	50	0.0000
0.0000	1.3356	100	0.0000
0.0000	2.0	150	0.0000
0.0000	2.6711	200	0.0000
0.0000	3.0	225	0.0000

Framework versions

Transformers 5.6.0
Pytorch 2.7.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

17Lab/qwen7b-full-sft-s13

qwen7b_full_sft_8gpu_s13

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

17Lab/
qwen7b-full-sft-s13